MétaCan
Menu
Back to cohort
Record W3021850714 · doi:10.5539/esr.v9n2p21

Economic Potential of Gold in Batouri (Eastern Cameroon)

2020· article· en· W3021850714 on OpenAlexvenueno aff
Tchouankam Klorane Junie, Mbog Michel Bertrand, Bayiga ElieConstant, Bernard Tassongwa, NgonNgon Gilbert François, Apouamoun Yiagnigni Roland, Kenfack JeanVictor, Jacques Étamé

Bibliographic record

VenueEarth Science Research · 2020
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
Fundersnot available
KeywordsRefining (metallurgy)Gold standard (test)Gold alloysMetallurgyChemistryEnvironmental scienceMining engineeringMineralogyEnvironmental chemistryGeologyMaterials scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

The aim of this work is the valorization of the economic potential of gold in the Batouri region. The study is undertaken on five sites of which two alluvials (Djengo and Mongonam localities) made up of flat and river gold, two eluvials (Kambele and Bote) containing gold of veins in quarries, and one semi mechanized exploitation (METALICON) working on the two previous  types. Laboratory works consist of traditional melting, determination of the various grades of gold through densimetry and spectrometry analysis and refining using the Miller Chloration method. The main results from these analyses are: i) recovery concentration is low, (about 0.5 g/t) for the traditional mining and higher with the semi mechanization (1.5-2 g/t). Densimetry and spectrometry analyses show that gold of semi mechanized sites has an average grade of about 24 carats, 22 carats and 20 for alluvial and eluvial gold respectively. ii) For 26 kg of gold refined, a weight of 16.681 kg is obtained at a cost of 4 051 946 (four million fifty one thousand and nine hundred forty six) CFA F. Spectrometry analyses reveal the presence of silver and copper impurities, elements that can still be valorized through the presence of a gold refining unit. Hence, the absence of a gold refining unit in our country leads to poor transformation of its ores and loss of devices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.332
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEarth Science ResearchSame topicMetal Extraction and BioleachingFrench-language works237,207